
"""simple docstring"""




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
  from ...onnx.config import PatchingSpec
  from ...tokenization_utils_base import PreTrainedTokenizerBase


A__ :       Union[str, Any]				= logging.get_logger(__name__)

A__ :       Tuple				= {
    'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
    'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
    'allenai/longformer-large-4096-finetuned-triviaqa': (
        'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
    ),
    'allenai/longformer-base-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
    'allenai/longformer-large-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
}


class       __magic_name__							(							snake_case_			):
       UpperCamelCase_ =		'longformer'
       def __init__(  self			,					A_ = 512			,					A_ = 2			,					A_ = 1			,					A_ = 0			,					A_ = 2			,					A_ = 3_0522			,					A_ = 768			,					A_ = 12			,					A_ = 12			,					A_ = 3072			,					A_ = "gelu"			,					A_ = 0.1			,					A_ = 0.1			,					A_ = 512			,					A_ = 2			,					A_ = 0.02			,					A_ = 1E-12			,					A_ = False			,					**A_			,					)  ->						int:





         """simple docstring"""




         super().__init__(pad_token_id=lowerCAmelCase__			,					**lowerCAmelCase__							)

         _lowercase:      Optional[int]   			=  attention_window
         _lowercase:      Union[str, Any]   			=  sep_token_id
         _lowercase:      Union[str, Any]   			=  bos_token_id
         _lowercase:      Union[str, Any]   			=  eos_token_id
         _lowercase:      Any   			=  vocab_size
         _lowercase:      Optional[int]   			=  hidden_size
         _lowercase:      List[Any]   			=  num_hidden_layers
         _lowercase:      Any   			=  num_attention_heads
         _lowercase:      Dict   			=  hidden_act
         _lowercase:      Dict   			=  intermediate_size
         _lowercase:      Tuple   			=  hidden_dropout_prob
         _lowercase:      Any   			=  attention_probs_dropout_prob
         _lowercase:      Dict   			=  max_position_embeddings
         _lowercase:      int   			=  type_vocab_size
         _lowercase:      Union[str, Any]   			=  initializer_range
         _lowercase:      Any   			=  layer_norm_eps
         _lowercase:      List[Any]   			=  onnx_export


class       __magic_name__							(							snake_case_			):
       def __init__(  self			,					A_			,					A_ = "default"			,					A_ = None							)  ->						str:





         """simple docstring"""




         super().__init__(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__							)
         _lowercase:      List[str]   			=  True
       @property
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         if self.task == "multiple-choice":
           _lowercase:      str   			=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
           _lowercase:      Dict   			=  {0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''global_attention_mask''', dynamic_axis),
             ]							)
       @property
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  super().outputs
         if self.task == "default":
           _lowercase:      Tuple   			=  {0: '''batch'''}
         return outputs
       @property
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         return 1E-4
       @property
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         return max(super().default_onnx_opset			,					14							)



       def 				lowercase_				(  self			,					A_			,					A_ = -1			,					A_ = -1			,					A_ = False			,					A_ = None			,					)  ->						Dict:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  super().generate_dummy_inputs(
             preprocessor=lowerCAmelCase__			,					batch_size=lowerCAmelCase__			,					seq_length=lowerCAmelCase__			,					is_pair=lowerCAmelCase__			,					framework=lowerCAmelCase__							)
         import torch

         # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
         # makes the export fail randomly
         _lowercase:      Dict   			=  torch.zeros_like(inputs['''input_ids''']							)
         # make every second token global
         _lowercase:      Dict   			=  1

         return inputs





'''simple docstring'''



import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.eager import context
							from tensorflow.python.framework import ops

							from transformers import GradientAccumulator, create_optimizer
@require_tf
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   int					):

													"""simple docstring"""
													self.assertEqual(len(lowerCAmelCase__					)					,     len(lowerCAmelCase__					)					)
													for a, b in zip(lowerCAmelCase__					,     lowerCAmelCase__					):
																				self.assertAlmostEqual(lowerCAmelCase__					,     lowerCAmelCase__					,     delta=lowerCAmelCase__					)







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      GradientAccumulator()
													accumulator([tf.constant([1.0, 2.0]					)]					)
													accumulator([tf.constant([-2.0, 1.0]					)]					)
													accumulator([tf.constant([-1.0, 2.0]					)]					)
													with self.assertRaises(lowerCAmelCase__					):
																				accumulator([tf.constant([1.0, 1.0]					), tf.constant([2.0, 2.0]					)]					)
													self.assertEqual(accumulator.step					,     3					)
													self.assertEqual(len(accumulator.gradients					)					,     1					)
													self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()					,     [-2.0, 5.0]					,     tol=1e-2					)
													accumulator.reset()
													self.assertEqual(accumulator.step					,     0					)
													self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()					,     [0.0, 0.0]					,     tol=1e-2					)







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
													ops.enable_eager_execution_internal()
													SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.config.list_physical_devices('''CPU'''					)
													if len(lowerCAmelCase__					) == 1:
																				tf.config.set_logical_device_configuration(
																				    physical_devices[0]					,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      tf.config.list_logical_devices(device_type='''CPU'''					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.distribute.MirroredStrategy(devices=devices[:2]					)

													with strategy.scope():
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      GradientAccumulator()
																				SCREAMING_SNAKE_CASE       :   Dict         =      tf.Variable([4.0, 3.0]					)
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[int]         =      create_optimizer(5e-5					,     10					,     5					)
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.Variable([0.0, 0.0]					,     trainable=lowerCAmelCase__					)

													def accumulate_on_replica(lowerCAmelCase__ :   List[str]					):
																				accumulator([gradient]					)

													def apply_on_replica():
																				optimizer.apply_gradients(list(zip(accumulator.gradients					,     [variable]					)					)					)

													@tf.function
													def accumulate(lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   List[Any]					):
																				with strategy.scope():
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      strategy.experimental_local_results(lowerCAmelCase__					)
																											local_variables[0].assign(lowerCAmelCase__					)
																											local_variables[1].assign(lowerCAmelCase__					)
																											strategy.run(lowerCAmelCase__					,     args=(gradient_placeholder,)					)

													@tf.function
													def apply_grad():
																				with strategy.scope():
																											strategy.run(lowerCAmelCase__					)

													def _check_local_values(lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   int					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      strategy.experimental_local_results(accumulator._gradients[0]					)
																				self.assertListAlmostEqual(values[0].value()					,     lowerCAmelCase__					,     tol=1e-2					)
																				self.assertListAlmostEqual(values[1].value()					,     lowerCAmelCase__					,     tol=1e-2					)

													accumulate([1.0, 2.0]					,     [-1.0, 1.0]					)
													accumulate([3.0, -1.0]					,     [-1.0, -1.0]					)
													accumulate([-2.0, 2.0]					,     [3.0, -2.0]					)
													self.assertEqual(accumulator.step					,     3					)
													_check_local_values([2.0, 3.0]					,     [1.0, -2.0]					)
													apply_grad()
													self.assertListAlmostEqual(variable.value()					,     [4.0, 3.0]					,     tol=1e-2					)
													accumulator.reset()
													self.assertEqual(accumulator.step					,     0					)
													_check_local_values([0.0, 0.0]					,     [0.0, 0.0]					)




